Michael Franklin UC Berkeley and Truviso, Inc.
Stream query processing is an emerging approach that enables data management technology to be better integrated into the fabric of network-intensive environments. For many applications, this technology can provide orders of magnitude performance improvement over traditional database systems, while retaining the benefits of SQL-based application development. Lately, stream processing has been moving from the research lab into the real world. In this talk, I'll survey the state of the art in stream query processing and related technologies such as event processing, discuss some of the implications for data-intensive system architectures, and provide my views on the future role of this technology from both a research and a commercial perspective, with an eye towards large-scale sensor deployments.
Speaker Bio
Michael Franklin is a Professor of Computer Science at UC Berkeley and is Co-Founder and Chief Technology Officer of Truviso, Inc., a start-up developing a new generation of real-time data management technology. At Berkeley his research focuses on the architecture and performance of distributed data management and information systems. His recent projects cover the areas of large-scale sensor networks, high-speed message brokering, scientific grid computing, and data and application integration. After a five-year stint as a database systems developer he attended the University of Wisconsin, Madison, where he received his Ph.D. in Computer Science in 1993. He was on the faculty at the University of Maryland before joining Berkeley in 1999. He is currently on the editorial boards of the ACM Journal of Data Quality and the VLDB Journal and is a trustee of the VLDB endowment. He is a Fellow of the Association for Computing Machinery and is a recipient of the National Science Foundation CAREER Award, the ACM SIGMOD "Test of Time" award, and most recently, the Best Paper award at the 2007 IEEE International Conference on Distributed Computing Systems.